Artificial Intelligence in the courts, legal academia and legal practice

Advances in technology, in particular in artificial intelligence, will continue to have a significant impact on the discipline of law in academia, the practicing profession and the courts. While technological forecasting is a dangerous game, current trends suggest that over the next 10 years there will likely be greater reliance on data analytic tools in assessing students, predicting judicial outcomes and making decisions about criminal defendants both pre- and post-conviction. There is also likely to be greater diffusion of expert systems offering standardised legal advice and legal documents, although it is less likely that there will be significant technological innovation in that field. There are significant differences between an artificial intelligence that mirrors doctrinal logic (expert systems) and an artificial intelligence based on projection from empirical observation (data analytics). Few legal professionals understand the mechanisms through which data analytics produces predictions. The limitations inherent and assumptions embedded in these tools are thus often poorly understood by those using them. This article explores the limitations of artificial intelligence technologies by considering the ways in which what they produce (for clients, law students and society) differs from what they replace. Ultimately, if we, as legal professionals, want to harness the benefits and limit the detriments of new artificial intelligence technologies, we need to understand what their limitations are, what assumptions are embedded within them and how they might undermine appropriate decision-making in legal practice, legal academia and, most crucially, the judiciary

Regulating in the Face of Socio-Technical Change

This chapter looks broadly at how lawyers and regulators should understand the relationship between regulation and changing technologies. It argues that instead of asking how we might “regulate technology” or “regulate new technology”, we should focus on the question of how we might institutionally manage the adjustment of law and regulation in light of ongoing socio-technical change. In particular, it argues that “technology” is neither a special rationale for nor a special object of regulation, but rather that it is changes in the socio-technical landscape that generate a need to constantly re-evaluate regulatory regimes. It concludes with high level principles that follow from this reframing for regulatory design, choice of regulatory institution, regulatory timing and regulatory responsiveness

Lecture Notes in Computer Science

We argue that electronic vote-counting software can engender broad-based public trust in elections to public office only if they are formally verified against their legal definition and only if they can produce an easily verifiable certificate for the correctness of the count. We then show that both are achievable for the Schulze method of votecounting, even when the election involves millions of ballots. We argue that our methodology is applicable to any vote-counting scheme that is rigorously specified. Consequently, the current practice of using unverified and unverifiable vote counting software for elections to public office is untenable. In particular, proprietary closed source vote-counting software is simply inexcusable